A Context-Specific Role for Retinoblastoma Protein-Dependent Negative Growth Control in Suppressing Mammary Tumorigenesis
Bibliographic record
Abstract
BACKGROUND: The ability to respond to anti-growth signals is critical to maintain tissue homeostasis and loss of this negative growth control safeguard is considered a hallmark of cancer. Negative growth regulation generally occurs during the G0/G1 phase of the cell cycle, yet the redundancy and complexity among components of this regulatory network has made it difficult to discern how negative growth cues protect cells from aberrant proliferation. METHODOLOGY/PRINCIPAL FINDINGS: The retinoblastoma protein (pRB) acts as the final barrier to prevent cells from entering into the cell cycle. By introducing subtle changes in the endogenous mouse Rb1 gene (Rb1(ΔL)), we have previously shown that interactions at the LXCXE binding cleft are necessary for the proper response to anti-growth signals such as DNA damage and TGF-β, with minimal effects on overall development. This disrupts the balance of pro- and anti-growth signals in mammary epithelium of Rb1(ΔL/ΔL) mice. Here we show that Rb1(ΔL/ΔL) mice are more prone to mammary tumors in the Wap-p53(R172H) transgenic background indicating that negative growth regulation is important for tumor suppression in these mice. In contrast, the same defect in anti-growth control has no impact on Neu-induced mammary tumorigenesis. CONCLUSIONS/SIGNIFICANCE: Our work demonstrates that negative growth control by pRB acts as a crucial barrier against oncogenic transformation. Strikingly, our data also reveals that this tumor suppressive effect is context-dependent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".